In [ ]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
%matplotlib inline
In [41]:
# Camera calibration - Udacity code
images = glob.glob('camera_cal/calibration*.jpg')
objpoints = []
imgpoints = []
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
for cal_files in images:
img = cv2.imread(cal_files)
grayscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(grayscale, (9,6), None)
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
# camera matrix & distortion coefficient
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (720, 1280), None, None)
In [42]:
# Test camera distortion correction
test_img = mpimg.imread('camera_cal/calibration1.jpg')
dst = cv2.undistort(test_img, mtx, dist, None, mtx)
bgr = cv2.cvtColor(dst, cv2.COLOR_RGB2BGR)
cv2.imwrite('output_images/calibration1_undistorted.jpg', bgr)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(test_img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [44]:
# Test camera distortion correction
test_img = mpimg.imread('test_images/test6.jpg')
dst = cv2.undistort(test_img, mtx, dist, None, mtx)
bgr = cv2.cvtColor(dst, cv2.COLOR_RGB2BGR)
cv2.imwrite('output_images/test6_undistorted.jpg', bgr)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(test_img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [45]:
# Perspective transform on chess board - Udacity code
def corners_unwarp(img, nx, ny, mtx, dist):
# Use the OpenCV undistort() function to remove distortion
undist = cv2.undistort(img, mtx, dist, None, mtx)
# Convert undistorted image to grayscale
gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
# Search for corners in the grayscaled image
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
if ret == True:
# Choose offset from image corners to plot detected corners
# This should be chosen to present the result at the proper aspect ratio
# My choice of 100 pixels is not exact, but close enough for our purpose here
offset = 100 # offset for dst points
# Grab the image shape
img_size = (gray.shape[1], gray.shape[0])
# For source points I'm grabbing the outer four detected corners
src = np.float32([corners[0], corners[nx-1], corners[-1], corners[-nx]])
# For destination points, I'm arbitrarily choosing some points to be
# a nice fit for displaying our warped result
# again, not exact, but close enough for our purposes
dst = np.float32([[offset, offset], [img_size[0]-offset, offset],
[img_size[0]-offset, img_size[1]-offset],
[offset, img_size[1]-offset]])
# Given src and dst points, calculate the perspective transform matrix
M = cv2.getPerspectiveTransform(src, dst)
# Warp the image using OpenCV warpPerspective()
warped = cv2.warpPerspective(undist, M, img_size)
# Return the resulting image and matrix
return warped, M
img = cv2.imread('camera_cal/calibration3.jpg')
nx = 9 # the number of inside corners in x
ny = 6 # the number of inside corners in y
top_down, perspective_M = corners_unwarp(img, nx, ny, mtx, dist)
bgr = cv2.cvtColor(top_down, cv2.COLOR_RGB2BGR)
cv2.imwrite('output_images/calibration3_topdown_perspective.jpg', bgr)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [46]:
# Perspective transform on road images
def bird_eye_view(img):
h, w = img.shape[0:2]
# Define trapezoid corner pixel x & y values in straight line image
# (290,670),(565,470),(720,470),(1030,670)
# Lower corners are raised to prevent the hood from being mapped onto the transformed image
src = np.float32([[290./1280*w,670./720*h], [565./1280*w,470./720*h], [720./1280*w,470./720*h], [1030./1280*w,670./720*h]])
# Map to top down view line end points for left lane and right lane
# (320,720),(320,0)(960,0)(960,720)
dst = np.float32([[w/4.,h], [w/4.,0], [3.*w/4.,0], [3.*w/4.,h]])
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
bird_eye = np.array(cv2.warpPerspective(img, M, (w, h)))
return bird_eye, M, Minv
test_img = mpimg.imread('test_images/test6.jpg')
undistorted = cv2.undistort(test_img, mtx, dist, None, mtx)
bgr = cv2.cvtColor(undistorted, cv2.COLOR_RGB2BGR)
cv2.imwrite('output_images/test6_undistorted.jpg', bgr)
bird_eye, perspective_M, inv_perspective_M = bird_eye_view(undistorted)
bgr = cv2.cvtColor(bird_eye, cv2.COLOR_RGB2BGR)
cv2.imwrite('output_images/test6_birdeye.jpg', bgr)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(undistorted)
ax1.set_title('Undistorted Original Image', fontsize=50)
ax2.imshow(bird_eye)
ax2.set_title('Bird Eye View Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [47]:
# Thresholded binary image - Udacity code, with modification to threshold values
def threshold_bin(img, s_thresh=(150, 255), sx_thresh=(20, 255)):
img = np.copy(img)
# Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
s_channel = hls[:,:,2]
# Threshold HLS saturation channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
# Sobel x
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold sobel x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
combined_binary = np.zeros_like(sxbinary)
combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
return combined_binary
image = mpimg.imread('test_images/test6.jpg')
undistorted = cv2.undistort(image, mtx, dist, None, mtx)
bird, perspective_M, inv_perspective_M = bird_eye_view(undistorted)
binary_output = threshold_bin(bird)
cv2.imwrite('output_images/test6_binary.jpg', binary_output*255)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(undistorted)
ax1.set_title('Original Undistorted Image', fontsize=40)
ax2.imshow(binary_output, cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [48]:
# Fit line polynomial - Udacity code
# Assuming you have created a warped binary image called "binary_output"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_output[binary_output.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_output, binary_output, binary_output))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_output.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_output.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_output.shape[0] - (window+1)*window_height
win_y_high = binary_output.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ploty = np.linspace(0, binary_output.shape[0]-1, binary_output.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
cv2.imwrite('output_images/test6_sliding_window_visualization.jpg', out_img)
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[48]:
In [49]:
# Visualization - Udacity code
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_output, binary_output, binary_output))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
cv2.imwrite('output_images/test6_polynomial_fit.jpg', result)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[49]:
In [50]:
# Find lane curvature - Udacity code
y_eval = np.max(ploty)
left_curverad = ((1 + (2*left_fit[0]*y_eval + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
right_curverad = ((1 + (2*right_fit[0]*y_eval + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
print(left_curverad, right_curverad)
In [53]:
# Convert radius of curvature to meters - Udacity code
# Define conversions in x and y from pixels space to meters
ym_per_pix = 3/150 # meters per pixel in y dimension
xm_per_pix = 3.7/670 # meters per pixel in x dimension
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
left_curverad = int(left_curverad)
right_curverad = int(right_curverad)
# Now our radius of curvature is in meters
print(left_curverad, 'm', right_curverad, 'm')
centerdist = '%.2f' % (((rightx[0] + leftx[0])/2.0 - 640)*xm_per_pix)
print (centerdist, 'm')
In [54]:
# Project line fit onto original image with text overlay - Udacity code with modifications
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_output).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, inv_perspective_M, (warp_zero.shape[1], warp_zero.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undistorted, 1, newwarp, 0.3, 0)
overlay_curve = "left curve radius " + str(left_curverad) + "m" + "; right curve radius " + str(right_curverad) + "m"
overlay_offset = "distance to center " + str(centerdist) + "m"
# use cv2.putText to overlay text with image
texted_image = cv2.putText(img=np.copy(result), text=overlay_curve, org=(50,50),fontFace=3, fontScale=1, color=(255,255,255), thickness=2)
texted_image = cv2.putText(img=np.copy(texted_image), text=overlay_offset, org=(50,100),fontFace=3, fontScale=1, color=(255,255,255), thickness=2)
bgr = cv2.cvtColor(texted_image, cv2.COLOR_RGB2BGR)
cv2.imwrite('output_images/test6_metadata_overlay.jpg', bgr)
plt.imshow(texted_image)
#plt.imshow(result)
Out[54]:
In [ ]:
# Video pipeline - Udacity code with modification
import os
from moviepy.editor import VideoFileClip
from IPython.display import HTML
# Define global parameters for storing line-fit polynomials, curvature and offset data to replaced "bad" frames
# Only the parameter values from the last single "good" frame is stored; the parameters are updated everytime a "good" frame is detected
first_frame = 0
left_fit_LG = None
right_fit_LG = None
left_fitx_LG = None
right_fitx_LG = None
left_curverad_LG = None
right_curverad_LG = None
centerdist_LG = None
skip_count = 0
def process_image(frame):
# Access globally defined variables in a function
global first_frame
global left_fit_LG
global right_fit_LG
global left_fitx_LG
global right_fitx_LG
global left_curverad_LG
global right_curverad_LG
global centerdist_LG
global skip_count
left_fit = None
right_fit = None
left_fitx = None
right_fitx = None
left_curverad = None
right_curverad = None
centerdist = None
undistorted = cv2.undistort(frame, mtx, dist, None, mtx)
bird, perspective_M, inv_perspective_M = bird_eye_view(undistorted)
binary_output = threshold_bin(bird)
# if it's the first frame in the video, or if the max number of allowed frame skips has been reached
# then run the sliding window search from scratch on the image
if first_frame == 0 or skip_count > 15:
# increment frame count once the first frame has been read to prevent this condition from triggering again
first_frame += 1
# reset skip frame counter to 0
skip_count = 0
histogram = np.sum(binary_output[binary_output.shape[0]//2:,:], axis=0)
out_img = np.dstack((binary_output, binary_output, binary_output))*255
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
nwindows = 9
window_height = np.int(binary_output.shape[0]/nwindows)
nonzero = binary_output.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
leftx_current = leftx_base
rightx_current = rightx_base
margin = 100
minpix = 50
left_lane_inds = []
right_lane_inds = []
for window in range(nwindows):
win_y_low = binary_output.shape[0] - (window+1)*window_height
win_y_high = binary_output.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ploty = np.linspace(0, binary_output.shape[0]-1, binary_output.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
y_eval = np.max(ploty)
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/670 # meters per pixel in x dimension
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
left_curverad = int(left_curverad)
right_curverad = int(right_curverad)
centerdist = '%.2f' % (((rightx[0] + leftx[0])/2.0 - 640)*xm_per_pix)
# whenever a new frame is searched from scratch, the data becomes a new reference, or starting point for future searches
left_fit_LG = left_fit
right_fit_LG = right_fit
left_fitx_LG = left_fitx
right_fitx_LG = right_fitx
left_curverad_LG = left_curverad
right_curverad_LG = right_curverad
centerdist_LG = centerdist
# for subsequent frames, unless maximum number of frames has been skipped, use the globally stored last good polynomial fit
# as a starting point to search for lane pixels in the current frame
else:
nonzero = binary_output.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# narrowly define margins to eliminate outliers that could bias line fit algorithms
margin = 20
# search for lane points within the band on either side of the previous polynomial fit function
left_lane_inds = ((nonzerox > (left_fit_LG[0]*(nonzeroy**2) + left_fit_LG[1]*nonzeroy + left_fit_LG[2] - margin)) & (nonzerox < (left_fit_LG[0]*(nonzeroy**2) + left_fit_LG[1]*nonzeroy + left_fit_LG[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit_LG[0]*(nonzeroy**2) + right_fit_LG[1]*nonzeroy + right_fit_LG[2] - margin)) & (nonzerox < (right_fit_LG[0]*(nonzeroy**2) + right_fit_LG[1]*nonzeroy + right_fit_LG[2] + margin)))
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ploty = np.linspace(0, binary_output.shape[0]-1, binary_output.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
y_eval = np.max(ploty)
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/670 # meters per pixel in x dimension
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
left_curverad = int(left_curverad)
right_curverad = int(right_curverad)
centerdist = '%.2f' % (((rightx[0] + leftx[0])/2.0 - 640)*xm_per_pix)
# Sanity check stage - compute the maximum pixel x-coordinate deviation on either the left or right lines
# between polynomial fit functions for the previous frame and current frame
left_dif = np.absolute(left_fitx - left_fitx_LG)
right_dif = np.absolute(right_fitx - right_fitx_LG)
# if the deviation exceeds a certain threshold, the frame is considered to be "bad" and skipped over
# the line fit, curvature, offset data is replaced by last good frame (stored by global variables)
if np.amax(left_dif) > 20 or np.amax(right_dif) > 20:
skip_count += 1
left_fitx = left_fitx_LG
right_fitx = right_fitx_LG
left_curverad = left_curverad_LG
right_curverad = right_curverad_LG
centerdist = centerdist_LG
# if the new polynomial fit lines are within reasonable threshold from previous frame, the new frame data is valid
else:
# reset skip frame counter, as current frame is a "good" frame
skip_count = 0
# update global parameters with data from current frame, it is now the latest reference for start of search in next frame
left_fit_LG = left_fit
right_fit_LG = right_fit
left_fitx_LG = left_fitx
right_fitx_LG = right_fitx
left_curverad_LG = left_curverad
right_curverad_LG = right_curverad
centerdist_LG = centerdist
warp_zero = np.zeros_like(binary_output).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
newwarp = cv2.warpPerspective(color_warp, inv_perspective_M, (warp_zero.shape[1], warp_zero.shape[0]))
result = cv2.addWeighted(undistorted, 1, newwarp, 0.3, 0)
overlay_curve = "left curve radius " + str(left_curverad) + "m" + "; right curve radius " + str(right_curverad) + "m"
overlay_offset = "distance to center " + str(centerdist) + "m"
texted_image = cv2.putText(img=np.copy(result), text=overlay_curve, org=(50,50),fontFace=3, fontScale=1, color=(255,255,255), thickness=2)
texted_image = cv2.putText(img=np.copy(texted_image), text=overlay_offset, org=(50,100),fontFace=3, fontScale=1, color=(255,255,255), thickness=2)
return texted_image
# extract frame from video, output new video with lane tracking mask and metadata overlay
project_output = 'output.mp4'
clip = VideoFileClip('project_video.mp4')
output_clip = clip.fl_image(process_image)
%time output_clip.write_videofile(project_output, audio=False)